1

Manufacturing Process

Performance

2.810

T. GutowskiSee Refs and extra slides at end

State of the Teams:

2

• Please form teams with 4-6 members

• We can help if you are unsure how to

proceed

3

Comparing Mfg Processes

Ref University Loughborough, UK

Conventional

casting

3D Printed

Guidelines: redesign for process, design for same objectives

4

Manufacturing process

performance attributes

• Cost - mostly direct costs, DFM, software

• Rate and Time – process rate & system rate

• Quality/Variation – physics & statistical control

• Energy/ CO2 – processes & systems

5

Manufacturing process

performance attributes

• Cost - DFM Boothroyd, Oct 2 Lewandowski

• Rate and Time – by process & for systems

Oct 30 Gershwin

• Quality/Variation – Oct 28 Hardt

• Energy/ CO2 – Oct 2, Nov 13

6

Profit = Sales - Costs

Ostwald

Direct

costs: “touch”

labor, direct

materials

& tooling…

Indirect

costs: common

activities that

support many

products

Our focus: the cost to make a product

7

Components of Direct Cost:

Direct Recurring Costs (Variable C = VN):

•Materials, including Buy to Fly, auxiliary…

•Labor, usually “touch”, but…

•Energy, usually small for parts

•Equipment (Prices as a “rental”)

Direct Non-recurring costs (Fixed C = F):

•Tooling, special equipment..

BMW

TRM tool

ORNL/

Cincinnati

BAAM

Machine

8

Unit cost: C/N =F/N + V

Serial processes takelonger, larger variable costsSpecialty mat’l add to variablecosts

Parallel processes require tooling,larger fixed costs, but short cycle time

N*

9

Estimate breakeven

N*

Waterjet

Extrusion

Direct Cost comparison;

breakeven part volume

Extrusion

• Fixed = extrusion die

• Variable =

(30grams/part)

• Material = aluminum

billet, scrap

• Labor = set-up & run

• Machine = set-up, run,

maintenance

Water jet

• Fixed = programing

• Variable = (30g part, 90

secs/part)

• Materials = aluminum

bar, waste, abrasive

• Labor = some

monitoring needed

• Machine = includes

machine, financing,

installation, maintenance

Aluminum Extrusion

• Extrusion die = $5,000

• Extrusion rate, less than 1 second to extrude one part

• Equipment rate ~ $40/hr

• Labor rate ~ $50/hr

• Materials billet aluminum,

some start up loss, kerf, say 30g + 1g waste => $0.31

• Setup/cleanup times dominates

Equip and Labor

• Sawing, post processing

• V = $0.31 + $0.09

• Cost = $5,000 + $0.40N

Nest Parts for Waterjet, waste

~30%

Waterjet cutting

• Variable Costs:

• Aluminum bar stock ~ $ 5/lb (~$10/kg)

• 30 gram part + waste = 40g/part = $0.40/ part

• Run time ~ 90 sec

• Abrasive ~ $0.30/part

• Machine cost (~$100k/5years/1shift (1000h/y)

+ maintenance) ~ $25/hr

• Labor cost ~ $35/hr

• Cost = $2.2N

Waterjet cutting

• Variable Cost Breakdown:

Variable Cost Value

Aluminum $0.4

Abrasive $0.3

Labor $0.875

Machine $0.625

Total $2.2

15

Estimated breakeven

N* ~ 3,000 parts

Breakeven cost $2.20/part

Waterjet

Extrusion

Big Blue Saw quote

16

17

18

Additive Mfg Vs Injection Mold

Ref. Cassandra Telenko et al

19

Components of Indirect Cost:

Indirect Costs

•Support activities that can be charged “evenly” to all products

i.e. “overhead” may include things like; design, programming,

maintenance, quality control, purchasing, marketing, sales,

general and administrative expenses…

e.g.Composites parts

may require more

inspection than

metal parts

20

Indirect costs..

• Become more important for higher

levels of automation,

• Become more difficult to allocate as the

number of products and variation grows.

• Use “Activity Based Costing” and other

tools

21

Cincinnati MAXIM FMS Cell

(5) Machines

(24) Pallets

(3) Shifts

22

System H; (2,000/35,000)

System 2;

(20/1,500)

System 3; (200/ 10,600)Part Types/ Total Produced

23

Cost Models• Time & Motion; F.W.Taylor, Ostwald, Polgar

• DFM and DFA: Boothroyd, Dewhurst & Knight (Parametric

Models)

• Software & On-Line Quotes

24

DFM guidelines are helpful,

they identify important

features that drive cost:

Examples-

• Handling and insertion

difficulty for manual assembly

• Injection mold tooling

complexity

25

Example: Time Estimation for

Manual Assembly• Handling

pick up

orient

• Insertion

location (obstructed view? Self locating?)

hold down and resistance

securing method

26

Symmetry

Handling Issues

0 180 180 90 360 360

0 0 90 180 0 360

Size Fragile/Sharp

Nest/TangleSlippery/Flexible

27

B-D Manual handling chart

Handling difficulties: nest, tangleslippery, sharp…

28

Insertion Issues

Obstructed Access/View

Alignment

Insertion Force

29

B-D Manual insertion chart

screws

30

1 – screw(2) (steel) not easy to align

2 – cover(steel) not easy to align – assembly worker’s fingers must be used to align edges

3 – spring(steel) (closed ends) subject to continuous cycling and

must be spring steel

4 – piston stop(plastic) edge is chamfered for ease of alignment5 – piston(aluminum) obstructed

access for insertion of spindle into bottom of bore

6 – main block(plastic) depth of bore is 28mm with small through hole for piston spindle

Pneumatic Piston Sub-Assembly

31

B-D Manual handling chart

piston

screws cover

spring

32

B-D Manual insertion chart A

coverpiston

spring

33

B-D Manual insertion chart B

screws

34

Pneumatic Piston Sub-Assembly

35

Boothroyd/Dewhurst Design Rules

1. Reduce part count and part types

2. Strive to eliminate adjustments

3. Design parts to be self-aligning and self-locating

4. Ensure adequate access and unrestricted vision

5. Ensure the ease of handling of parts from bulk

6. Minimize the need for reorientations during assembly

7. Design parts that cannot be installed incorrectly

8. Maximize part symmetry if possible or make parts obviously asymmetrical

36

Rules to reduce part count

1. During operation of the product, does the part move relative to all other parts already assembled?

Only gross motion should be considered – small motions that can be accommodated by elastic hinges, for example, are not sufficient for a positive answer

2. Must the part be of a different material than or be isolated from all other parts already assembled?

Only fundamental reasons concerned with material properties are acceptable

3. Must the part be separate from all other parts already assembled because otherwise necessary assembly or disassembly of other separate parts would be impossible?

37

1 – snap on cover and stop (plastic)

2 – spring(steel)

3 – piston (aluminum)

4 – main block (plastic)

Redesign: Pneumatic Piston Sub-Assembly

38

Re-design

Redesigns need to be thoroughly checked that they

do not compromise the functioning of the device!

Process Rate Limits

• Machining – limits on (MRR), large forces…

• Injection Molding – die development, heat

transfer

• Sheet metal forming – die development, part

complexity of CNC machines, multiple presses

• Assembly – time between steps, out of tolerance

39

40

More on this later

Nominal Mfg Process Rates

Vary by more than 8 orders

41

See extra slides at end

The consequences of slow

machines

• Bottlenecks

• Large capital investment

42

Metal Additive Part

String ribbon process for PV

43

Production Rate

• Process rate - physics

• System rate – waiting for needed resource

44

Flow efficiency

= process time/total

time in the system

45

System Time & Rate

• Oct 30, Dr. Stan Gershwin

– Little’s Law,

– unreliable machines,

– Buffers (zero, infinite and finite)

– M/M/1 queue

– Simulations

Quality Issues at the Process

46

Satisfied customer and meeting target specification,

later TPS and hidden problems

47

Where you find the problem

matters

• Product recalls

• Customer complaints

• Warranty claims

• Dealer returns

• In-house inspection

• Assembly

• Observed at the process

Increasing

CostRepair and upgrade of the Hubble Telescope

Fix problems close to home!

Quality Loss Function

48Ref M.Phadke

After G Taguchi

Deviations from

The target value

should be penalized

Inspecting in Quality

49

Ref M. Phadke

50Kalpakjian and Schmid 7th ed

Nominal Process Variation(process in conrol)

Surface RoughnessDimensional Tolerance

51

Process variation/tolerance

What are the

most important

variables?

Kalpakjian

52

Process variation/tolerance

elevated temperature

machining

53

Process variation

size

temperature

54

Quality Control Actions

Y = (+, U)

Ref Hardt, 2001

55

How to control variation

output variationdisturbance

sensitivitycontrol action

ref. D. Hardt

56

Real Time Control

output variationdisturbance

sensitivitycontrol action

Covered in 2.830, D. Hardt

57

Designed Experiments

output variationdisturbance

sensitivitycontrol action

Output

feature

Controllable parameter

58

Statistical Process Control

output variationdisturbance

sensitivitycontrol action

59

Mean drift, and variance of the

output Y

Mean on target, but

large variation due to

many random effects

Mean drift has

assignable cause,

tight grouping

means small variation

60

Comparing the variation with

the specifications

Goals: 6s < (USL-LSL)

and mean centered

out of spec

partsout of spec

parts

61

If UCL-LCL = 6s

and the process mean

is in the center, then

The out of compliance

parts are given by

2(0.500-(3s)) =

2(0.500-0.4987) =

0.0026 or 0.26% or 2600ppm

62

Some propose a process capability index Cp that

compares the tolerance interval USL-LSL vs the

process variation 6s.

Upper Specification Limit

Lower Specification Limit

Target

Cp % out ppm

⅔ 4.55 45,500

1 0.26 2600

1 ⅓ .0063 63

2 .00034 3.4

63

Mean Drift

Upper Specification Limit

Lower Specification Limit

Target

0.135%

13.6

+2.1

+0.135

15.835

+0.135

15.97%

2.810 Manufacturing

Processes and

Systems - Process

Control64 Slide taken from D. Hardt

“Not In-Control”

i

i+1

i+2

...

The Parent

Distribution

Changes with Time

65

Statistical Control Methods

Strategy:

1. Monitor process when under control

2. Determine Centerline & Control Limits

3. Data outside of UCL/LCL indicates change

4. Investigate and eliminate causes of change

66

SPC chart showing

“Not-in-Control” condition

• X bar chart

Lower Control Limit

Sampling period

Upper Control Limit

Centerline

Read 1.5-1 Hogg & Ledolter, “Control Charts”

67

Histogram for CNC Turning

From Dave Hardt

68

Sampling Frequency

Factors that determine the appropriate

sampling frequency:

– Stability of process

– Potential loss

– Cost of sampling inspection

69

What causes variation in dimensions?

• Machine variation

– e.g. change in settings, environment, equipment

• Material variation

– e.g.suppliers, substitutes, mixtures..

• Operator variation

– Jim instead of Joe, or Alice instead of Mary

• Method variation

– Late Friday afternoon, Mary always does it this

way…

70

“x-bar charts”Mean of the means

• Mean

Where, n = sample size

k = number of samples

A2 = constant from Table C.1

R = defined next slideAlso see alternative method

Presented by Dave Hardt

71

“R-charts” Range = high - low

Where, n = sample size

k = number of samples

D3 , D4 = constants from Table C.1

• Standard Deviation can be estimated from R

72

Estimate of standard deviation from range

ref. P. Lyonnet

73

74

75

Robustness Vs Sensitivity

• Mean shift by 1s requires on average

44 data points to observe

• Alternatives

– ±2s; two consecutive observations

– ±1s; three consecutive observations

– other charts (e.g. “Cusum”)

76

Source Identification; Ishikawa

Cause and Effect DiagramMan Machine

Material Method

Effect

Finding the cause of a disturbance is the most difficult part of quality control. There are only aids to help you with this problem solving exercise like the Ishikawa Diagrams which helps you cover all categories, and the “5 Whys” which helps you go to the root cause.

77

SPC - Statistical Process Control in Injection

Molding and Extrusion By Chris Rauwendaal

78

References

• Boothroyd & Dewhurst DFA (manual)

• Control Charts (sect 1.5-1), Hogg & Ledolter

• also see your text, topics are listed in the index. For

the 7th edition: SPC section 36.8, Cost section 40.10,

and Energy sections 40.5 &40.6 these topics are also

listed in other places in the text

• “hockey stick diagram” for energy and rate, Gutowski

et al, See “ electrical energy used in manufacturing

processes” p 1586-1588 your text